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Record W4286461916 · doi:10.3390/brainsci12080959

Tolerance to Stimulant Medication for Attention Deficit Hyperactivity Disorder: Literature Review and Case Report

2022· review· en· W4286461916 on OpenAlexaff
Kenneth Handelman, Fernando Sumiya

Bibliographic record

VenueBrain Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStimulantAttention deficit hyperactivity disorderMedicineDrug tolerancePsychiatryPsychology

Abstract

fetched live from OpenAlex

Recommended treatment for attention deficit hyperactivity disorder (ADHD) includes stimulant medication. While these medicines are effective for most ADHD patients, benefits may wear off, suggesting tolerance. This paper reviews the published literature on tolerance to stimulant medication treatment for ADHD. As there are relatively few studies published, pivotal studies and ADHD treatment guidelines were also reviewed. Research demonstrates physiological changes related to continued stimulant usage in neurons and certain brain regions, suggesting a mechanism for tolerance development. One clinical study showed that 24.7% of patients developed tolerance to stimulants in the time of days to weeks; another showed 2.7% developed tolerance over 10 years. Long term follow-up studies demonstrate that medication response may lessen over longer durations of treatment in a high percentage of patients. Strategies to manage tolerance include switching stimulant medicines, drug holidays, or clinical reassessment. Three cases illustrate challenges with treating patients who develop tolerance to stimulant medication. The paucity of research and lack of guidance to clinicians may contribute to significant under recognition of tolerance to stimulant medication. Further research is required to define clinical tolerance for stimulants in ADHD and to provide guidance on identifying and managing tolerance in clinical practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.424
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2022
Admission routes1
Has abstractyes

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